Abstract

We introduce a collaborative anti-spam system that is based on pervasive global social email networks. Essentially, we provide a solution to this open research problem: given a network of N users who are willing to share information collaboratively (e.g. the digests or ngerprints of known spams), how do we search for each user's content e ciently and reliablyinadistributedmannerwithminimal tra c cost on the network? As a solution to this open problem, our proposed system employs the percolation search process, which makes the tra c generated due to queries for spamdigestsscalesublinearlyasafunctionof N. However, in order to reap the bene ts of this novel percolation search algorithm, the node degree distribution of the underlying network must be heavy-tailed. Interestingly, latent global social email networks comprising of personal contacts possess a power-law heavy-tailed degree distribution, which renders itself an ideal natural platform to employ the percolation search algorithm. As a result, our proposed distributed spam lter requires no dedicated peer-to-peer (P2P) systems or centralized server-based systems. We have performed large-scale simulations and we nd that the system achieves a spam detectionratecloseto100%, whilethefalsepositiverateiskeptaroundzero. Thebandwidth costperuseraswellasthesystem-widebandwidth cost are shown to be very low.

Citations